Code Division Multiple Access (CDMA) systems are based on a digital wideband spread spectrum technology in which multiple independent user signals are transmitted across an allocated segment of the available radio spectrum. In CDMA, each user signal comprises a different orthogonal code and a pseudo random binary sequence that modulates a carrier, thereby spreading the spectrum of the waveform and thus allowing a large number of user signals to share the same frequency spectrum. The user signals are separated in the receiver with a correlator which allows only the signal with the selected orthogonal code to be de-spread. Other user signals whose codes do not match are not de-spread and as such contribute to system noise. In 3rd generation Wideband CDMA (WCDMA) different spreading factors and variable user data rates can be supported simultaneously.
By the use of spreading codes, the frequency band of a transmission signal is spread to a chip rate, which is larger than the actual data or information symbol array. For example, if the used spreading code has the length of eight data symbols (referred to as “chips”), eight chips are transmitted for every data symbol. The property of unique codes is given by the property of orthogonality of the spreading codes meaning in mathematical terms that the inner product or correlation respectively of the spreading codes used or to use for communication is zero. Orthogonality of the spreading codes guarantees that transmission of a signal or sequence of data symbols respectively which is coded by a spreading code neither creates or propagates side effects to other signals coded by other orthogonal spreading codes and corresponding to other users of a communication system. A receiver looking for a certain spreading code of a certain transmitter will take signals coded by orthogonal spreading codes as a noise of the radio frequency (RF) channel. Since spreading codes can have different length, the property of orthogonality must be given also for spreading codes of different lengths.
FIG. 1 shows a graphical representation of an orthogonal variable spreading factor (OVSF) tree, which can be used for construction of a spreading code, wherein the abbreviation “SF” designates the spreading factor characterizing the length of the spreading code and the level of the OVSF tree. Within each tree level, the available spreading codes have the same length and are orthogonal. The spreading factor may also be expressed by at the ratio between chip rate and data symbol rate or between chip duration and data symbol duration. Spreading codes of different users may fall into different levels in an OVSF tree thus providing various levels of quality of service (QoS). User symbols may be spread by spreading factors ranging from 4 to 512.
Channel equalization is especially interesting for the downlink of the newly introduced third generation Universal Mobile Telecommunication System with frequency-division duplex (FDD-UMTS). In contrast to the uplink transmission, the signals of all users transmitted from the base station are received synchronously at a specific mobile terminal. Separation between user-signals is achieved by applying unique orthogonal spreading codes to each of them. Multipath propagation destroys the orthogonality, causing strong multiple-access interference (MAI) and inter-symbol interference (ISI) for small spreading factors. Hence, by equalizing the received chip sequence, the orthogonality between user-signals can be restored. Especially for high data rates services like High Speed Downlink Packet Access (HSDPA), the performance of conventional rake receiver is limited. This receiver coherently combines the different receiving paths but cannot suppress the intra-cell interference due to the non-zero cross-correlations between the arbitrarily time-shifted spreading codes assigned to the different users. Advanced receivers are necessary to mitigate the severe effect of MAI and ISI.
The signal-to-noise ratio (SNR) is broadly defined as the ratio of the desired signal power to the noise power and has been accepted as a standard measure of signal quality. Adaptive system design requires the estimate of SNR in order to modify the transmission parameters to make efficient use of system resources. Poor channel conditions, reflected by low SNR values, require that the transmitter modify transmission parameters such as coding rate, modulation mode etc. to compensate channel distortions and to satisfy certain application dependent constraints such as constant bit error rate (BER) and throughput. Dynamic system parameter adaptation requires a real-time noise power estimator for continuous channel quality monitoring and corresponding compensation in order to maximize resource utilization. SNR knowledge also provides information about the channel quality, which can be used by handoff algorithms, power control, channel estimation through interpolation, and optimal soft information generation for high performance decoding algorithms. The SNR can be estimated using regularly transmitted training sequences, pilot data or data symbols (blind estimation).
However, white noise is rarely the case in practical wireless communication systems where the noise is dominated by interferences, which are often colored in nature. Therefore, it has been proposed to estimate noise variances at each subcarrier to obtain estimates of noise-plus-interference variance and calculate a resultant signal-to-noise-plus-interference (SINR) ratio. These noise-plus-interference estimates are specifically useful for adaptive modulation, and optimal soft value calculation for improving channel decoder performance. Moreover, it can be used to detect and avoid narrowband interference.
Hence, the goal is to estimate the variance of the noise-plus-interference over the symbol estimates in the mobile terminal, e.g., UMTS user equipment, where the term “noise” is intended to mean additive white Gaussian noise which can be modeled to have two components: thermal noise due to random motion of electrons in the receiver circuitry and the intercell (co-channel) interference originating from the surrounding cells. Additionally, the term “interference” is intended to mean distortion coming from the codes in the same cell with the code of interest whose symbols we are estimating.
The U.S. Pat. No. 6,957,175 B2 discloses a method and apparatus for estimating a signal-to-interference ratio (SIR) of baseband signals which are received and processed by a data demodulator to provide demodulated signals to a SIR estimator. The SIR estimator receives the demodulated symbols from the data demodulator and estimates the average signal power of the demodulated symbols as a function of a median based average power value and a mean based average power value of the demodulated symbols for each quadrant of a quadrature phase shift keying (QPSK) constellation. The SIR estimator estimates the average effective interference power of the demodulated symbols and calculates the SIR by dividing the estimated average signal power of the demodulated symbols by the estimated average effective interference power of the demodulated symbols. However, this known estimation scheme is very complex, since it uses a very complicated median filtering operation which requires buffering a lot of QPSK symbol estimate samples, a sorting mechanism, median filtering operations which result in much buffering and computational complexity burden.